[HTML][HTML] Model aggregation techniques in federated learning: A comprehensive survey
Federated learning (FL) is a distributed machine learning (ML) approach that enables
models to be trained on client devices while ensuring the privacy of user data. Model …
models to be trained on client devices while ensuring the privacy of user data. Model …
Transitioning From Federated Learning to Quantum Federated Learning in Internet of Things: A Comprehensive Survey
Quantum Federated Learning (QFL) recently becomes a promising approach with the
potential to revolutionize Machine Learning (ML). It merges the established strengths of …
potential to revolutionize Machine Learning (ML). It merges the established strengths of …
AiFed: An adaptive and integrated mechanism for asynchronous federated data mining
With the growing concerns on data security and user privacy, a decentralized mechanism is
implemented for federated data mining (FDM), which can bridge data silos and collaborate …
implemented for federated data mining (FDM), which can bridge data silos and collaborate …
Federated and asynchronized learning for autonomous and intelligent things
The Internet of Things (IoT) intertwined with autonomous and intelligent things (AITs) is
beginning to affect many aspects of our daily lives. Along with this trend, asynchronous …
beginning to affect many aspects of our daily lives. Along with this trend, asynchronous …
A federated platform enabling a systematic collaboration among devices, data and functions for smart mobility
Through the vast adoption and application of emerging technologies, the intelligence and
autonomy of smart mobility can be substantially elevated to address more diversified …
autonomy of smart mobility can be substantially elevated to address more diversified …
Async-HFL: Efficient and robust asynchronous federated learning in hierarchical IoT networks
Federated Learning (FL) has gained increasing interest in recent years as a distributed on-
device learning paradigm. However, multiple challenges remain to be addressed for …
device learning paradigm. However, multiple challenges remain to be addressed for …
SLMFed: A stage-based and layer-wise mechanism for incremental federated learning to assist dynamic and ubiquitous IoT
Along with the vast application of Internet of Things (IoT) and the ever-growing concerns
about data protection, a novel type of learning, named incremental federated learning (IFL) …
about data protection, a novel type of learning, named incremental federated learning (IFL) …
FMGCN: Federated Meta Learning-Augmented Graph Convolutional Network for EV Charging Demand Forecasting
Recent booming successes of electric vehicles (EVs) motivate emerging exploration of
spatio-temporal EV charging demand forecasting to inform policy making. Recent studies …
spatio-temporal EV charging demand forecasting to inform policy making. Recent studies …
Asynchronous Wireless Federated Learning with Probabilistic Client Selection
Federated learning (FL) is a promising distributed learning framework where distributed
clients collaboratively train a machine learning model coordinated by a server. To tackle the …
clients collaboratively train a machine learning model coordinated by a server. To tackle the …
Afm3d: An asynchronous federated meta-learning framework for driver distraction detection
Driver Distraction Detection (3D) is of great significance in helping intelligent vehicles
decide whether to remind drivers or take over the driving task and avoid traffic accidents …
decide whether to remind drivers or take over the driving task and avoid traffic accidents …